Year: 2021
Type: article
Abstract: Type Ia supernovae (SNe Ia) are used as distance indicators to infer the cosmological parameters that specify the expansion history of the universe. Parameter inference depends on the criteria by which the analysis SN sample is selected. Only for the simplest selection criteria and population models can the likelihood be calculated analytically, otherwise it needs to be determined numerically, a process that inherently has error. Numerical errors in the likelihood lead to errors in parameter inference. This article presents toy examples where the distance modulus is inferred given a set of SNe at a single redshift. Parameter estimators and their uncertainties are calculated using Monte Carlo techniques. The relationship between the number of Monte Carlo realizations and numerical errors is presented. The procedure can be applied to more realistic models and used to determine the computational and data management requirements of the transient analysis pipeline.
Source: The Open Journal of Astrophysics
Author: Alex G. Kim
Institution: Lawrence Berkeley National Laboratory
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: diamond
Funders: National Science Foundation, U.S. Department of Energy, National Energy Research Scientific Computing Center, Centre National de la Recherche Scientifique, Office of Science, Science and Technology Facilities Council, Institut National de Physique Nucléaire et de Physique des Particules, High Energy Physics
Awards: G1141912172, G122673668, G1229526393, G1286236842, G1414775356, G1677143136, G1816284028, G1863003419, G2371548206, G303061748, G3083819904, G365575497, G419881185, G4201897825, G42916606, G4501827968, G4654690619, G495301720, G498139845, G5076365615, G5296923526, G5438923238, G5570207115, G6447494072, G6652958823, G7214672632, G759921648, G805243471, G8382000481, G8726098666, G969889393, G97770923
OpenAlex ID: W3045061126